Evaluating Acoustic and Linguistic Features of Detecting Depression Sub-Challenge Dataset
Evaluating Acoustic and Linguistic Features of Detecting Depression Sub-Challenge Dataset
复制标题
评估检测抑郁子挑战数据集的声学和语言特征
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
R. Ghomi
中科院分区：
文献类型：
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作者：
Larry Zhang;Joshua Driscol;Xiaotong Chen;R. Ghomi
抑郁症影响着全世界数亿人。随着抑郁症患病率的增加，该疾病的经济成本显着增加。 AVEC 2019 用 AI（人工智能）检测抑郁症子挑战赛提供了一个机会，利用新颖的信号处理、机器学习和人工智能技术，通过声学、语音语言内容和面部表情等数字生物标记来预测个体抑郁症的存在和严重程度。在我们的分析中，我们指出了在预处理和建模过程中需要考虑的关键因素，以有效构建抑郁症的语音生物标志物。我们还验证了数据集的人口统计和严重性评分分布的平衡，以评估我们结果的普遍性。
Depression affects hundreds of millions of individuals world wide. With the prevalence of depression increasing, economic costs of the illness are growing significantly. The AVEC 2019 Detecting Depression with AI (Artificial Intelligence) Sub-Challenge provides an opportunity to use novel signal processing, machine learning, and artificial intelligence technology to predict the presence and severity of depression in individuals through digital biomarkers such as vocal acoustics, linguistic contents of speech, and facial expression. In our analysis, we point out key factors to consider during pre-processing and modelling to effectively build voice biomarkers for depression. We additionally verify the dataset for balance in demographic and severity score distribution to evaluate the generalizability of our results.